Topic
Visual analytics
About: Visual analytics is a research topic. Over the lifetime, 7353 publications have been published within this topic receiving 156213 citations.
Papers published on a yearly basis
Papers
3 Sep 1996
TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
Abstract: A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts).
5,833 citations
Book•
1 Jan 1999TL;DR: In this paper, the authors present a method for using vision to think in higher-level visualisation, focusing on space, interaction, focus + context, text, and context.
Abstract: 1. Information Visualization 2. Space 3. Interaction 4. Focus + Context 5. Data Mapping: Text 6. Higher-Level Visualization 7. Using Vision to Think 8. Applications and Innovations 9. Conclusion Bibliography Index
3,796 citations
1 Jan 2005
TL;DR: This is the book form of the Research and Development Agenda for Visual Analytics to be published by IEEE in 2005.
Abstract: This is the book form of the Research and Development Agenda for Visual Analytics to be published by IEEE in 2005.
2,293 citations
2,084 citations
AT&T1
TL;DR: This paper proposes a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique.
Abstract: Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.
2,001 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 142 |
| 2024 | 254 |
| 2023 | 267 |
| 2022 | 450 |
| 2021 | 382 |
| 2020 | 492 |